Domain Adaptation Kernel Learning

Eds pattern recognition applications and methods.

Domain adaptation kernel learning. Domain adaptation aims to correct the mismatch in statistical properties between the source domain on which a classifier is trained and the target domain to which the classifier is to be applied. De marsico m di baja g fred a. Kernel learning to obtain the best feature kernel pairing. 2018 domain adaptation transfer learning by kernel representation adaptation.

Kernel learning directly learns a nonparametric kernel matrix subject to some proper similarity constraints. Overview o motivation and problem definition o multiple kernel learning o soft margin learning for multiple feature kernel combination sml mfkc o domain adaptation da o proposed technology o results and discussion o conclusion references. A nonlinear learning literature called kernel learning is closely related to our work. Geodesic flow kernel for unsupervised domain adaptation abstract.

Our proposed technique employs a effective kernel selection by multiple kernel learning mkl method to choose the optimal kernel to be used along with unsupervised domain adaptation method in the reproducing kernel hilbert space rkhs for a solution to the problem. Our framework referred to as kernel learning for domain adaptation learning kldal simultaneously learns an optimal kernel space and a robust classifier by minimizing both the structural risk. In real world applications of visual recognition many factors such as pose illumination or image quality can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. Several transfer learning works had been designed based on domain invariant kernel matrix.

Domain adaptation is a field associated with machine learning and transfer learning this scenario arises when we aim at learning from a source data distribution a well performing model on a different but related target data distribution. Classification algorithms developed with data from one domain cannot be directly used in another related domain and hence adaptation of either the classifier or the data representation becomes strictly imperative for example there is actually strong evidence that a significant. In this paper we address the challenging scenario of unsupervised domain adaptation where the target domain does not provide any annotated data to assist in adapting the classifier.